Discovering genomic rearrangements under selection in serious ovarian cancer

复发的基因融合和内部串联复制是最具肿瘤特异性的分子之一 已知的标记物可以提供潜在的治疗靶点。除了几个值得注意的例外， 然而，相对常见的复发基因融合并未在常见病例中被鉴定出来。 癌症，通常有多个复杂的染色体重排，很难用 传统的细胞遗传学方法。复杂的肿瘤核型使使用 细胞遗传学，但暗示了产生融合或内部串联的反复重排的可能性 复制(ITD)可能很普遍。这项建议旨在使用深度测序和新颖的分析 描述了研究浆液性卵巢癌基因组和转录组的某些方面的技术 由于技术或分析方法的限制，以及测试个人内部和个人之间的限制，仍处于隐藏状态 对肿瘤的选择性压力。本提案的主要内容如下：1)进一步研究 卵巢癌中基因重排的程度，重点是发现局部重排 转录成RNA；2)确定我拥有的一组新的循环转录本的组成 最近发现在正常和致病的人类细胞中表达水平相对较高；3) 表征卵巢癌的双分钟，结合生物信息学确定重排 它们的序列组成和统计分析，以确定其组成的进化压力 肿瘤所产生的影响。申请人有成功发现新的基因融合的记录。 超高通量测序(ESRRA-C11或F20融合)，以及原创严谨的设计 超高通量数据的统计和生物信息学方法。在高通量基因组技术和用于分析这些技术的统计方法的先驱帕特里克·O·布朗博士的指导下，申请者将继续职业发展和培训。该项目的第一个目标将在指导阶段实施，并将试行目标2和目标3的实验。K99/R00奖项将支持申请者发展成为一名独立调查人员。

Recurrent gene fusions and internal tandem duplications are among the most tumor-specific molecular markers known and can provide the potential for therapeutic targets. With a few notable exceptions, however, relatively common recurrent gene fusions have not been identified in commonly occurring carcinomas, which often have multiple, complex chromosomal rearrangements that are difficult to analyze by traditional cytogenetic approaches. Complex tumor karyotpes make it difficult to identify gene fusions using cytogenetics, but suggest the possibility that recurrent rearrangements producing fusions or internal tandem duplications (ITDs) may be prevalent. This proposal aims to use deep sequencing and the novel analytic techniques described to study aspects of the serous ovarian cancer genome and transcriptome which have remained hidden due to limitations in technology or analytical methods, and to test intra-individual and inter-individual selective pressures on tumors. The aspects of this proposal are as follows 1) to further investigate the extent of gene rearrangements in ovarian cancer, focusing on discovering local rearrangements transcribed into RNA; 2) to determine the composition of a group of novel circular transcripts that I have recently found to be expressed at relatively high levels in normal and pathogenic human cells; 3) to characterize double minutes in ovarian cancer, combining bioinformatics to determine rearrangements in their sequence composition and statistical analysis to determine evolutionary pressures on their composition exerted by the tumors. The applicant has a track-record of success in discovering novel gene fusions with ultra-high throughput sequencing (the ESRRA-C11 orf20 fusion), as well as designing original rigorous statistical and bioinformatic methods for ultra-high throughput data. Under the mentorship of Dr. Patrick O. Brown, a pioneer in high throughput genomic technologies and statistical methods for analyzing them, the applicant will continue career development and training. The first aim of this project will be performed during the mentoring phase, and experiments for aims 2 and 3 will be piloted. The K99/R00 award will support the applicant in her development into an independent investigator.